東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Mathematical modeling and intelligen...

Hammouch, Zakia.

FindBook

Google Book

Amazon

博客來

Mathematical modeling and intelligent control for combating pandemics

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Mathematical modeling and intelligent control for combating pandemics/ edited by Zakia Hammouch, Mohamed Lahby, Dumitru Baleanu.
其他作者:	Hammouch, Zakia.
出版者:	Cham :Springer Nature Switzerland : : 2023.,
面頁冊數:	xiv, 272 p. :ill. (some col.), digital ;24 cm.
內容註:	Part. 1. Mathematical Modeling and analysis for Covid-19 Pandemic -- Chapter. 1. An Extended Fractional SEIR Model to Predict the Spreading Behavior of COVID-19 Disease using Monte-Carlo Back Sampling -- Chapter. 2. Dynamics and optimal control methods for the COVID-19 model -- Chapter. 3. Optimal Strategies to Prevent COVID-19 from Becoming a Pandemic -- Chapter. 4. Modeling and analysis of COVID-19 based on a deterministic compartmental model and Bayesian inference -- Chapter. 5. Predicting the Infection Level of Covid-19 Virus using Normal Distribution Based Approximation Model and PSO -- Chapter. 6. An Optimal Vaccination Scenario for COVID-19 Transmission Between Children and Adults -- Part. 2. Intelligent Control Techniques and Covid-19 Pandemic -- Chapter. 7. The Role of Artificial Intelligence and Machine Learning for the Fight Against COVID-19 -- Chapter. 8. Coronavirus Lung Image Classification with Uncertainty Estimation using Bayesian Convolutional Neural Networks -- Chapter. 9. Identify Unfavorable COVID Medicine Reactions From The Three-Dimensional Structure By Employing Convolutional Neural Network -- Chapter. 10. Using Reinforcement Learning for optimizing COVID-19 vaccine distribution strategies -- Chapter. 11. Incorporating Contextual Information and Feature Fuzzification for Effective Personalized Healthcare Recommender System -- Chapter. 12. Prediction of Growth and Review of Factors influencing the Transmission of COVID-19 -- Chapter. 13. COVID-19 Combating Strategies and Associated Variables for its Transmission: An approach with multi-criteria decision-making techniques in the Indian context -- Chapter. 14. Crisis management, Internet and AI: Information in the age of COVID-19, and future pandemics.
Contained By:	Springer Nature eBook
標題:	Intelligent control systems. -
電子資源:	https://doi.org/10.1007/978-3-031-33183-1
ISBN:	9783031331831

Mathematical modeling and intelligent control for combating pandemics
Mathematical modeling and intelligent control for combating pandemics[electronic resource] /edited by Zakia Hammouch, Mohamed Lahby, Dumitru Baleanu. - Cham :Springer Nature Switzerland :2023. - xiv, 272 p. :ill. (some col.), digital ;24 cm. - Springer optimization and its applications,v. 2031931-6836 ;. - Springer optimization and its applications ;v. 203..

Part. 1. Mathematical Modeling and analysis for Covid-19 Pandemic -- Chapter. 1. An Extended Fractional SEIR Model to Predict the Spreading Behavior of COVID-19 Disease using Monte-Carlo Back Sampling -- Chapter. 2. Dynamics and optimal control methods for the COVID-19 model -- Chapter. 3. Optimal Strategies to Prevent COVID-19 from Becoming a Pandemic -- Chapter. 4. Modeling and analysis of COVID-19 based on a deterministic compartmental model and Bayesian inference -- Chapter. 5. Predicting the Infection Level of Covid-19 Virus using Normal Distribution Based Approximation Model and PSO -- Chapter. 6. An Optimal Vaccination Scenario for COVID-19 Transmission Between Children and Adults -- Part. 2. Intelligent Control Techniques and Covid-19 Pandemic -- Chapter. 7. The Role of Artificial Intelligence and Machine Learning for the Fight Against COVID-19 -- Chapter. 8. Coronavirus Lung Image Classification with Uncertainty Estimation using Bayesian Convolutional Neural Networks -- Chapter. 9. Identify Unfavorable COVID Medicine Reactions From The Three-Dimensional Structure By Employing Convolutional Neural Network -- Chapter. 10. Using Reinforcement Learning for optimizing COVID-19 vaccine distribution strategies -- Chapter. 11. Incorporating Contextual Information and Feature Fuzzification for Effective Personalized Healthcare Recommender System -- Chapter. 12. Prediction of Growth and Review of Factors influencing the Transmission of COVID-19 -- Chapter. 13. COVID-19 Combating Strategies and Associated Variables for its Transmission: An approach with multi-criteria decision-making techniques in the Indian context -- Chapter. 14. Crisis management, Internet and AI: Information in the age of COVID-19, and future pandemics.

The contributions in this carefully curated volume, present cutting-edge research in applied mathematical modeling for combating COVID-19 and other potential pandemics. Mathematical modeling and intelligent control have emerged as powerful computational models and have shown significant success in combating any pandemic. These models can be used to understand how COVID-19 or other pandemics can spread, analyze data on the incidence of infectious diseases, and predict possible future scenarios concerning pandemics. This book also discusses new models, practical solutions, and technological advances related to detecting and analyzing COVID-19 and other pandemics based on intelligent control systems that assist decision-makers, managers, professionals, and researchers. Much of the book focuses on preparing the scientific community for the next pandemic, particularly the application of mathematical modeling and intelligent control for combating the Monkeypox virus and Langya Henipavirus.

ISBN: 9783031331831

Standard No.: 10.1007/978-3-031-33183-1doiSubjects--Topical Terms:

546464
Intelligent control systems.

LC Class. No.: RA652.2.M3

Dewey Class. No.: 362.1969015118

Mathematical modeling and intelligent control for combating pandemics
LDR:03883nmm a2200337 a 4500 001 2334805
003 DE-He213
005 20230911163429.0
006 m d
007 cr nn 008maaau
008 240402s2023 sz s 0 eng d
020 $a 9783031331831 $q (electronic bk.)
020 $a 9783031331824 $q (paper)
024 7 $a 10.1007/978-3-031-33183-1 $2 doi
035 $a 978-3-031-33183-1
040 $a GP $c GP
041 0 $a eng
050 4 $a RA652.2.M3
072 7 $a GPFC $2 bicssc
072 7 $a SCI064000 $2 bisacsh
072 7 $a GPFC $2 thema
082 0 4 $a 362.1969015118 $2 23
090 $a RA652.2.M3 $b M426 2023
245 0 0 $a Mathematical modeling and intelligent control for combating pandemics $h [electronic resource] / $c edited by Zakia Hammouch, Mohamed Lahby, Dumitru Baleanu.
260 $a Cham : $b Springer Nature Switzerland : $b Imprint: Springer, $c 2023.
300 $a xiv, 272 p. : $b ill. (some col.), digital ; $c 24 cm.
490 1 $a Springer optimization and its applications, $x 1931-6836 ; $v v. 203
505 0 $a Part. 1. Mathematical Modeling and analysis for Covid-19 Pandemic -- Chapter. 1. An Extended Fractional SEIR Model to Predict the Spreading Behavior of COVID-19 Disease using Monte-Carlo Back Sampling -- Chapter. 2. Dynamics and optimal control methods for the COVID-19 model -- Chapter. 3. Optimal Strategies to Prevent COVID-19 from Becoming a Pandemic -- Chapter. 4. Modeling and analysis of COVID-19 based on a deterministic compartmental model and Bayesian inference -- Chapter. 5. Predicting the Infection Level of Covid-19 Virus using Normal Distribution Based Approximation Model and PSO -- Chapter. 6. An Optimal Vaccination Scenario for COVID-19 Transmission Between Children and Adults -- Part. 2. Intelligent Control Techniques and Covid-19 Pandemic -- Chapter. 7. The Role of Artificial Intelligence and Machine Learning for the Fight Against COVID-19 -- Chapter. 8. Coronavirus Lung Image Classification with Uncertainty Estimation using Bayesian Convolutional Neural Networks -- Chapter. 9. Identify Unfavorable COVID Medicine Reactions From The Three-Dimensional Structure By Employing Convolutional Neural Network -- Chapter. 10. Using Reinforcement Learning for optimizing COVID-19 vaccine distribution strategies -- Chapter. 11. Incorporating Contextual Information and Feature Fuzzification for Effective Personalized Healthcare Recommender System -- Chapter. 12. Prediction of Growth and Review of Factors influencing the Transmission of COVID-19 -- Chapter. 13. COVID-19 Combating Strategies and Associated Variables for its Transmission: An approach with multi-criteria decision-making techniques in the Indian context -- Chapter. 14. Crisis management, Internet and AI: Information in the age of COVID-19, and future pandemics.
520 $a The contributions in this carefully curated volume, present cutting-edge research in applied mathematical modeling for combating COVID-19 and other potential pandemics. Mathematical modeling and intelligent control have emerged as powerful computational models and have shown significant success in combating any pandemic. These models can be used to understand how COVID-19 or other pandemics can spread, analyze data on the incidence of infectious diseases, and predict possible future scenarios concerning pandemics. This book also discusses new models, practical solutions, and technological advances related to detecting and analyzing COVID-19 and other pandemics based on intelligent control systems that assist decision-makers, managers, professionals, and researchers. Much of the book focuses on preparing the scientific community for the next pandemic, particularly the application of mathematical modeling and intelligent control for combating the Monkeypox virus and Langya Henipavirus.
650 0 $a Intelligent control systems. $3 546464
650 1 4 $a Systems Theory, Control. $3 893834
650 2 4 $a Applications of Mathematics. $3 890893
650 0 $a Pandemics $x Prevention. $3 3631330
650 0 $a Pandemics $x Mathematical models. $3 3666727
700 1 $a Hammouch, Zakia. $3 3487752
700 1 $a Lahby, Mohamed. $3 3593027
700 1 $a Baleanu, D. $q (Dumitru) $3 1573621
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
830 0 $a Springer optimization and its applications ; $v v. 203. $3 3666726
856 4 0 $u https://doi.org/10.1007/978-3-031-33183-1
950 $a Mathematics and Statistics (SpringerNature-11649)